Reginald S. Fletcher¹, Daniel K. Fisher²

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¹ Crop Production Systems Research Unit, Agricultural Research Service, United States Department of Agriculture, Stoneville, USA.
² Sustainable Water Management Research Unit, Agricultural Research Service, United States Department of Agriculture, Stoneville, USA.
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Abstract: There is a growing interest in the Open Ag community to use inexpensive sensors controlled by open-source software to measure plant height and plant canopy temperature of agricultural crops. Plant height and plant canopy temperature are key indicators of plant health. This research study reports on an ongoing research initiative to test a compact and inexpensive mobile sensor to measure plant height and plant canopy temperature. The system is controlled by open source software and hardware. The specific objectives for this study were to analyze the relationship between plant height and plant canopy temperature of soybeans (Glycine max L.) measured with the mobile system and to analyze the spatial correlation of the plant height and plant canopy temperature measurements. Data were collected in a soybean plot in 2018 and 2019. Descriptive statistics, Pearson correlation, and geostatistical techniques were used to evaluate the data. A negative statistically significant (p ≤ 0.05) relationship was observed between the plant height and the plant canopy temperature measurements (r = −0.54, 2018; r = −0.37, 2019). Also, both parameters were spatially correlated; however, plant height had a greater spatial continuity than plant canopy temperature. Furthermore, similar patterns were observed for the in-field variability of the plant height and plant temperature maps derived via kriging. Similarities in plant height and plant canopy temperatures were observed from one year to the next, suggesting that the sensor technologies could be used as a historical record for monitoring growth patterns in soybean fields. The sensors and techniques used in this study can be easily adapted to other crops, thus providing two important layers for monitoring plant growth and potentially plant stress.

Keywords: Glycine max, Ultrasonic Sensors, Infrared Thermometer, Correlation, In-Field Variability

Cite this paper: Fletcher, R. and Fisher, D. (2019) Spatial Analysis of Soybean Plant Height and Plant Canopy Temperature Measured with On-the-Go Tractor Mounted Sensors. Agricultural Sciences, 10, 1486-1496. doi: 10.4236/as.2019.1011109.

References

[1]   Jackson, R.D. (1982) Canopy Temperature and Crop Water Stress. Advances in Irrigation, 1, 43-85.
https://doi.org/10.1016/B978-0-12-024301-3.50009-5

[2]   Clawson, K.L., Jackson, R.D. and Pinter, P.J. (1989) Evaluating Plant Water Stress with Canopy Temperature Differences. Agronomy Journal, 81, 858-863.
https://doi.org/10.2134/agronj1989.00021962008100060004x

[3]   Rashid, A.J., Stark, C., Tanveer, A. and Mustafa, T. (1999) Use of Canopy Temperature Measurements as a Screening Tool for Drought Tolerance in Spring Wheat. Journal of Agronomy and Crop Science, 182, 231-237.
https://doi.org/10.1046/j.1439-037x.1999.00335.x

[4]   Portz, G., Amaral, L., Molin, J. and Adamchuk, V. (2013) Field Comparison of Ultrasonic and Canopy Reflectance Sensors Used to Estimate Biomass and N-Uptake in Sugarcane. Precision Agriculture 2013-Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013, 111-117.

[5]   Fisher, D.K. and Huang, Y. (2017) Mobile Open-Source Plant-Canopy Monitoring System. Modern Instrumentation, 6, 1-13.
https://doi.org/10.4236/mi.2017.61001

[6]   Sui, R. and Baggard, J. (2018) Center-Pivot-Mounted Sensing System for Monitoring Plant Height and Canopy Temperature. Transactions American Society of Agricultural and Biological Engineers, 61, 831-837.
https://doi.org/10.13031/trans.12506

[7]   Scotford, I.M. and Miller, P.C.H. (2004) Combination of Spectral Reflectance and Ultrasonic Sensing to Monitor the Growth of Winter Wheat. Biosystems Engineering, 87, 27-38.
https://doi.org/10.1016/j.biosystemseng.2003.09.009

[8]   Chang, Y.K., Zaman, Q.U., Rehaman, T.U., Farooque, A.A., Esau, T. and Jameel, M.W. (2017) A Real-Time Ultrasonic System to Measure Wild Blueberry Plant Height during Harvesting. Biosystems Engineering, 157, 35-44.
https://doi.org/10.1016/j.biosystemseng.2017.02.004

[9]   Fisher, D.K. and Sui, R. (2013) An Inexpensive Open-Source Ultrasonic Sensing System for Monitoring Liquid Levels. Agricultural Engineering International: CIGR Journal, 15, 328-334.

[10]   Bitella, G., Rossi, R., Bochicchio, R., Perniola, M. and Amato, M. (2014) A Novel Low-Cost Open-Hardware Platform for Monitoring Soil Water Content and Multiple Soil-Air-Vegetation Parameters. Sensors, 14, 19639-19659.
https://doi.org/10.3390/s141019639

[11]   Mesas-Carrascosa, F.J., VerdúSantano, D., Meroño, J.E., Sánchez de la Orden, M. and García-Ferrer, A. (2015) Open Source Hardware to Monitor Environmental Parameters in Precision Agriculture. Biosystems Engineering, 137, 73-83.
https://doi.org/10.1016/j.biosystemseng.2015.07.005

[12]   Trilles, S., Torres-Sospedra, J., Belmonte, O., Zarazaga-Soria, F.J., González-Pérez, A. and Huerta, J. (2019) Development of an Open Sensorized Platform in a Smart Agriculture Context: A Vineyard Support System for Monitoring Mildew Disease. Sustainable Computing: Informatics and Systems, 1-36.
https://doi.org/10.1016/j.suscom.2019.01.011

[13]   NOAA National Centers for Environmental Information, Climate at a Glance: County Time Series.
https://www.ncdc.noaa.gov/cag/

[14]   United States Department of Agriculture, National Agricultural Statistics Service. (2019) Washington County Mississippi, 2017 Census of Agriculture County Profile. 2.

[15]   Rees, D.G. (2001) Essential Statistics. 4th Edition, Chapman and Hall/CRC, Boca Raton, FL.

[16]   R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
https://www.R-project.org/

[17]   Oliver, M. and Webster, R. (2014) A Tutorial Guide to Geostatistics: Computing and Modelling Variograms and Kriging. Catena, 113, 56-69.
https://doi.org/10.1016/j.catena.2013.09.006

[18]   Oliver, M. and Webster, R. (2015) Basics Steps in Geostatistics: The Variogram and Kriging. Springer Briefs in Agriculture. Springer, Cham, Heidelberg, New York.
https://doi.org/10.1007/978-3-319-15865-5

[19]   QGIS Development Team (2019) QGIS Geographic Information System. Open Source Geospatial Foundation Project.
http://qgis.osgeo.org

[20]   Bai, G., Ge, Y., Hussain, W., Baenziger, P.S. and Graef, G. (2016) A Multi-Sensor System for High Throughput Field Phenotyping in Soybean and Wheat Breeding. Computers and Electronics in Agriculture, 128, 181-192.
https://doi.org/10.1016/j.compag.2016.08.021

[21]   Colaço, A.F., Molin, J.P., Rosell-Polo, J.R. and Escolà, A. (2019) Spatial Variability in Commercial Orange Groves. Part 1: Canopy Volume and Height. Precision Agriculture, 20, 788-804.
https://doi.org/10.1007/s11119-018-9612-3